{"id":"W3046648263","doi":"","title":"Daydream: Accurately Estimating the Efficacy of Optimizations for {DNN} Training","year":2020,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Computer science; Profiling (computer programming); Software deployment; Artificial neural network; Software; Artificial intelligence; Machine learning; Graph; Dependency graph; Parallel computing; Computer engineering; Theoretical computer science; Software engineering; Programming language","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001888215,0.00195332,0.0004956772,0.001253276,0.0005200769,0.0009809635,0.002087542,0.001118225,0.001759131],"category_scores_gemma":[0.01528019,0.0009125615,0.0006391457,0.0007560822,0.0008213831,0.002772001,0.0008238892,0.002467963,0.0009455492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001990785,"about_ca_system_score_gemma":0.002511465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01726136,"about_ca_topic_score_gemma":0.03267249,"domain_scores_codex":[0.9988481,0.0002646934,0.00007403506,0.0003464901,0.0003172311,0.0001494312],"domain_scores_gemma":[0.9942645,0.003506018,0.000365202,0.001024708,0.0006626018,0.0001770081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006268247,0.0002643213,0.02711595,0.0003578423,0.0001874481,0.0001212851,0.0001414716,0.8176409,0.01004435,0.003330945,0.01605089,0.1241178],"study_design_scores_gemma":[0.00001763143,0.00005872297,0.001492862,0.00001306087,0.00001342301,0.00001887308,0.00001676454,0.9877769,0.007590431,0.001887927,0.001099261,0.00001409784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6294591,0.002226044,0.2936504,0.00119392,0.0004210895,0.0002049289,0.005660637,0.05799032,0.009193508],"genre_scores_gemma":[0.827184,0.0004601235,0.1610132,0.0004708854,0.00004363515,0.00020615,0.005837709,0.002388031,0.002396282],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01726136,"threshold_uncertainty_score":0.03432178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2144990876850486,"score_gpt":0.246582360473011,"score_spread":0.03208327278796241,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}